Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.

Single-and multiwalled carbon nanotubes dispersed in nematic liquid crystal (LC) solvents are found to be orientationally ordered by the nematic matrix. Procedures are demonstrated for the preparation of monolayer and multilayer nanotube films with organization controlled using well-established methods of LC alignment, including grooved surfaces, magnetic fields, and patterned electrodes. LC alignment should be possible with other nanometer-sized building blocks, offering a general route for controlled assembly of organized nanomaterials and devices.

We report the improved production of recombinant proteins in E. coli , reliant on tightly controlled autoinduction, triggered by phosphate depletion in stationary phase. The method, reliant on engineered strains and plasmids, enables improved protein expression across scales.Expression levels using this approach have reached as high as 55% of total cellular protein.Initial use of the method in instrumented fed batch fermentations enables cell densities of~30 grams dry cell weight (gCDW) per liter and protein titers up to 8.1+/-0.7 g/L (~270 mg/gCDW).The process has also been adapted to an optimized autoinduction media, enabling routine batch production at culture volumes of 20 L (384 well plates), 100 L (96 well plates), 20 mL and 100 mL. In batch cultures, cells densities routinely reach~5-7 gCDW per liter, offering protein titers above 2 g/L. The methodology has been validated with a set of diverse heterologous proteins and is of general use for the facile optimization of routine protein expression from high throughput screens to fed-batch fermentation.

Abstract-Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense.Index Terms-Cardiac magnetic resonance imaging (MRI), four-dimensional (4-D), level-set, segmentation, temporal model.

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